Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment

Increasing concentrations of greenhouse gases in the atmosphere are expected to modify the global water cycle with significant consequences for terrestrial hydrology. We assess the impact of climate change on hydrological droughts in a multimodel experiment including seven global impact models (GIMs) driven by biascorrected climate from five global climate models under four representative concentration pathways (RCPs). Drought severity is defined as the fraction of land under drought conditions. Results show a likely increase in the global severity of hydrological drought at the end of the 21st century, with systematically greater increases for RCPs describing stronger radiative forcings. Under RCP8.5, droughts exceeding 40% of analyzed land area are projected by nearly half of the simulations. This increase in drought severity has a strong signal-to-noise ratio at the global scale, and Southern Europe, the Middle East, the Southeast United States, Chile, and South West Australia are identified as possible hotspots for future water security issues. The uncertainty due to GIMs is greater than that from global climate models, particularly if including a GIM that accounts for the dynamic response of plants to CO2 and climate, as this model simulates little or no increase in drought frequency. Our study demonstrates that different representations of terrestrial water-cycle processes in GIMs are responsible for a much larger uncertainty in the response of hydrological drought to climate change than previously thought. When assessing the impact of climate change on hydrology, it is therefore critical to consider a diverse range of GIMs to better capture the uncertainty

a) Schematic of the impact vs likelihood matrix, derived from the NSWWS. The alert code depends on both the uncertainty of the forecast (along the columns) and the strength of the impact (along the rows); b) Schematic showing the relation between the RR (excess deaths) and the temperature during summer (in degrees C).

<p>The temperature range is adjusted to reproduce a RR between 1.0 and 1.16 by using the linear relationship.</p

Semi-operational first guess alerts for summer 2014.

<p>As in Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137804#pone.0137804.g002" target="_blank">2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137804#pone.0137804.g003" target="_blank">3</a>, the forecast dates are along the top, and the issue dates down the side. “Z” refers to Zulu Time as is the same as UTC. Dates with alerts are circled in the figure. The image in the bottom left shows the local forecast for South East England on 18–19 July, issued on 17 July. In the top right of the smaller image one can see the matrix indicating the most severe type of event forecast in that region. Note that forecasts issued on 16 July are missing due to the system going down on that day.</p

of the alerts issued during March 2013 by the current system in use in the Heatwave Plan for England.

<p>The regional risk of meeting heatwave action trigger temperatures is in percentage and is shown for each of the 9 NSWWS regions.</p

Case study for August 2013.

<p>The first guess alerts generated by the prototype system are shown along each row (for the day after up to 4 days ahead). The days are rearranged to be always the same along the columns. The color scheme is explained with the matrix at the bottom right of the figure (where the forecast lead time is used as a proxy for forecast uncertainty). The bottom left panel illustrates an example of a detailed regional alert for the South-West. Scotland, Wales and Northern Ireland are left blank as they do not share the same alert system for health forecasting.</p